We use a ResNet-18 architecture [1]} modified to accommodate the MCDropout sampling procedure. The modification consists of adding a dropout layer with dropout probability \(p=0.2\)  after each convolutional layer. We train with an SGD optimizer with a momentum of \(0.9\)  and a weight decay of \(0.00001\) . The learning rate is following a triangular scheduler, first increasing linearly from 0 to \(0.2\)  during the first half of the epochs, and then decreasing linearly back to 0. We train for a total of 30 epochs, reaching a test accuracy of \(89\%\) . When using MCDropout, we sample 20 models and average the output distributions. The images of Fig. REF  are taken randomly from the CIFAR100 test images with an MCDropout predicted probability larger than \(0.9\) .
